
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_    :			Dict    =	logging.get_logger(__name__)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=False    )	->						Optional[Any]:
				UpperCamelCase_:				Optional[Any]						=    []
				for i in range(config.num_hidden_layers    ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')    )
								rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')    )
								rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')    )
								rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')    )
								rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')    )
								rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')    )
								rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')    )
								rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')    )
								rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')    )
								rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')    )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("""cls_token""", """vit.embeddings.cls_token"""),
				        ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
				        ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
				        ("""pos_embed""", """vit.embeddings.position_embeddings"""),
				    ]    )

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("""norm.weight""", """layernorm.weight"""),
								        ("""norm.bias""", """layernorm.bias"""),
								        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
								        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
								    ]    )

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								UpperCamelCase_:				Optional[Any]						=    [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""    ) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("""norm.weight""", """vit.layernorm.weight"""),
								        ("""norm.bias""", """vit.layernorm.bias"""),
								        ("""head.weight""", """classifier.weight"""),
								        ("""head.bias""", """classifier.bias"""),
								    ]    )

				return rename_keys







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=False    )	->						Tuple:
				for i in range(config.num_hidden_layers    ):
								if base_model:
												UpperCamelCase_:				Any						=    """"""
								else:
												UpperCamelCase_:				List[Any]						=    """vit."""
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								UpperCamelCase_:				Any						=    state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''    )
								UpperCamelCase_:				Optional[Any]						=    state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''    )
								# next, add query, keys and values (in that order) to the state dict
								UpperCamelCase_:				Union[str, Any]						=    in_proj_weight[
								    : config.hidden_size, :
								]
								UpperCamelCase_:				Optional[Any]						=    in_proj_bias[: config.hidden_size]
								UpperCamelCase_:				Any						=    in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								UpperCamelCase_:				Dict						=    in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								UpperCamelCase_:				Optional[Any]						=    in_proj_weight[
								    -config.hidden_size :, :
								]
								UpperCamelCase_:				str						=    in_proj_bias[-config.hidden_size :]







def 							A__       (    lowerCamelCase    )	->						List[str]:
				UpperCamelCase_:				Dict						=    ["""head.weight""", """head.bias"""]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Tuple:
				UpperCamelCase_:				Union[str, Any]						=    dct.pop(lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    val







def 							A__       (    )	->						str:
				UpperCamelCase_:				Union[str, Any]						=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:				int						=    Image.open(requests.get(lowerCamelCase  ,     stream=lowerCamelCase    ).raw    )
				return im







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				UpperCamelCase_:				str						=    ViTConfig()
				UpperCamelCase_:				Optional[Any]						=    False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								UpperCamelCase_:				List[str]						=    True
								UpperCamelCase_:				Optional[Any]						=    int(vit_name[-12:-10]    )
								UpperCamelCase_:				int						=    int(vit_name[-9:-6]    )
				else:
								UpperCamelCase_:				int						=    10_00
								UpperCamelCase_:				Any						=    """huggingface/label-files"""
								UpperCamelCase_:				Union[str, Any]						=    """imagenet-1k-id2label.json"""
								UpperCamelCase_:				Dict						=    json.load(open(hf_hub_download(lowerCamelCase  ,     lowerCamelCase  ,     repo_type="""dataset"""    )  ,     """r"""    )    )
								UpperCamelCase_:				Tuple						=    {int(lowerCamelCase    ): v for k, v in idalabel.items()}
								UpperCamelCase_:				Optional[int]						=    idalabel
								UpperCamelCase_:				int						=    {v: k for k, v in idalabel.items()}
								UpperCamelCase_:				Union[str, Any]						=    int(vit_name[-6:-4]    )
								UpperCamelCase_:				Optional[Any]						=    int(vit_name[-3:]    )
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith("""tiny"""    ):
												UpperCamelCase_:				List[str]						=    1_92
												UpperCamelCase_:				int						=    7_68
												UpperCamelCase_:				Optional[int]						=    12
												UpperCamelCase_:				str						=    3
								elif vit_name[9:].startswith("""small"""    ):
												UpperCamelCase_:				List[Any]						=    3_84
												UpperCamelCase_:				Optional[Any]						=    15_36
												UpperCamelCase_:				List[Any]						=    12
												UpperCamelCase_:				Optional[Any]						=    6
								else:
												pass
				else:
								if vit_name[4:].startswith("""small"""    ):
												UpperCamelCase_:				int						=    7_68
												UpperCamelCase_:				Optional[Any]						=    23_04
												UpperCamelCase_:				str						=    8
												UpperCamelCase_:				Optional[Any]						=    8
								elif vit_name[4:].startswith("""base"""    ):
												pass
								elif vit_name[4:].startswith("""large"""    ):
												UpperCamelCase_:				List[Any]						=    10_24
												UpperCamelCase_:				List[Any]						=    40_96
												UpperCamelCase_:				Optional[int]						=    24
												UpperCamelCase_:				Tuple						=    16
								elif vit_name[4:].startswith("""huge"""    ):
												UpperCamelCase_:				Dict						=    12_80
												UpperCamelCase_:				Optional[Any]						=    51_20
												UpperCamelCase_:				List[Any]						=    32
												UpperCamelCase_:				str						=    16

    # load original model from timm
				UpperCamelCase_:				int						=    timm.create_model(lowerCamelCase  ,     pretrained=lowerCamelCase    )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				UpperCamelCase_:				Union[str, Any]						=    timm_model.state_dict()
				if base_model:
								remove_classification_head_(lowerCamelCase    )
				UpperCamelCase_:				Dict						=    create_rename_keys(lowerCamelCase  ,     lowerCamelCase    )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
				read_in_q_k_v(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								UpperCamelCase_:				Tuple						=    ViTModel(lowerCamelCase    ).eval()
				else:
								UpperCamelCase_:				str						=    ViTForImageClassification(lowerCamelCase    ).eval()
				model.load_state_dict(lowerCamelCase    )

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								UpperCamelCase_:				List[str]						=    DeiTImageProcessor(size=config.image_size    )
				else:
								UpperCamelCase_:				Any						=    ViTImageProcessor(size=config.image_size    )
				UpperCamelCase_:				Union[str, Any]						=    image_processor(images=prepare_img()  ,     return_tensors="""pt"""    )
				UpperCamelCase_:				str						=    encoding["""pixel_values"""]
				UpperCamelCase_:				Union[str, Any]						=    model(lowerCamelCase    )

				if base_model:
								UpperCamelCase_:				Union[str, Any]						=    timm_model.forward_features(lowerCamelCase    )
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(lowerCamelCase  ,     outputs.pooler_output  ,     atol=1E-3    )
				else:
								UpperCamelCase_:				List[str]						=    timm_model(lowerCamelCase    )
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCamelCase  ,     outputs.logits  ,     atol=1E-3    )

				Path(lowerCamelCase    ).mkdir(exist_ok=lowerCamelCase    )
				print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''    )
				model.save_pretrained(lowerCamelCase    )
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
				image_processor.save_pretrained(lowerCamelCase    )


if __name__ == "__main__":
							lowerCamelCase_    :			Optional[int]    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--vit_name""",
							    default="""vit_base_patch16_224""",
							    type=str,
							    help="""Name of the ViT timm model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)

							lowerCamelCase_    :			List[Any]    =	parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








from ..utils import is_flax_available, is_torch_available


if is_torch_available():
							from .autoencoder_kl import AutoencoderKL
							from .controlnet import ControlNetModel
							from .dual_transformer_ad import DualTransformeraDModel
							from .modeling_utils import ModelMixin
							from .prior_transformer import PriorTransformer
							from .ta_film_transformer import TaFilmDecoder
							from .transformer_ad import TransformeraDModel
							from .unet_ad import UNetaDModel
							from .unet_ad import UNetaDModel
							from .unet_ad_condition import UNetaDConditionModel
							from .unet_ad_condition import UNetaDConditionModel
							from .vq_model import VQModel

if is_flax_available():
							from .controlnet_flax import FlaxControlNetModel
							from .unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .vae_flax import FlaxAutoencoderKL






